Exponential synchronization of multiple impulsive discrete-time memristor-based neural networks with stochastic perturbations and time-varying delays
作者： Huiyuan Li , Jian-an Fang , Xiaofan Li , Tingwen Huang
DOI: 10.1016/J.NEUCOM.2020.01.110
关键词: Computer science 、 Memristor 、 Discrete time and continuous time 、 Set (abstract data type) 、 Matrix (mathematics) 、 Control theory 、 Class (set theory) 、 Exponential synchronization 、 Artificial neural network 、 Synchronization 、 Synchronization (computer science)
摘要: Abstract This paper investigates the impulsive synchronization problems of a class multiple discrete-time memristor-based neural networks (DTMNNs) with stochastic perturbations and time-varying delays. The include preservation for stable DTMNNs under achievement unstable control. Firstly, we construct Secondly, two sets sufficient conditions are respectively established by constructing appropriate Lyapunov functionals using different methods. Under perturbations, is achieved as long first set satisfied. Moreover, desired control matrix derived solving second conditions. Finally, in order to verify effectiveness theoretical results, present numerical examples.
sci-hub.se 参考文章(57)
Anding Dai, Wuneng Zhou, Yuhua Xu, Cuie Xiao, Adaptive exponential synchronization in mean square for Markovian jumping neutral-type coupled neural networks with time-varying delays by pinning control Neurocomputing. ,vol. 173, pp. 809- 818 ,(2016) , 10.1016/J.NEUCOM.2015.08.034
Cheng-De Zheng, Ziping Wei, Zhanshan Wang, Robustly adaptive synchronization for stochastic Markovian neural networks of neutral type with mixed mode-dependent delays Neurocomputing. ,vol. 171, pp. 1254- 1264 ,(2016) , 10.1016/J.NEUCOM.2015.07.066
Wenbing Zhang, Yang Tang, Qingying Miao, Jian-An Fang, Synchronization of stochastic dynamical networks under impulsive control with time delays. IEEE Transactions on Neural Networks. ,vol. 25, pp. 1758- 1768 ,(2014) , 10.1109/TNNLS.2013.2294727
Jiejie Chen, Zhigang Zeng, Ping Jiang, Global Mittag-Leffler stability and synchronization of memristor-based fractional-order neural networks Neural Networks. ,vol. 51, pp. 1- 8 ,(2014) , 10.1016/J.NEUNET.2013.11.016
Gai Sun, Yu Zhang, Exponential stability of impulsive discrete-time stochastic BAM neural networks with time-varying delay Neurocomputing. ,vol. 131, pp. 323- 330 ,(2014) , 10.1016/J.NEUCOM.2013.10.010
Shukai Duan, Wenfeng Hu, Chuandong Li, Sichao Wu, Exponential Stability of Discrete-Time Delayed Hopfield Neural Networks with Stochastic Perturbations and Impulses Results in Mathematics. ,vol. 62, pp. 73- 87 ,(2012) , 10.1007/S00025-011-0131-Z
Xiaodi Li, R. Rakkiyappan, P. Balasubramaniam, Existence and global stability analysis of equilibrium of fuzzy cellular neural networks with time delay in the leakage term under impulsive perturbations Journal of The Franklin Institute-engineering and Applied Mathematics. ,vol. 348, pp. 135- 155 ,(2011) , 10.1016/J.JFRANKLIN.2010.10.009
J.S. Baras, N.S. Patel, Robust control of set-valued discrete-time dynamical systems IEEE Transactions on Automatic Control. ,vol. 43, pp. 61- 75 ,(1998) , 10.1109/9.654887
Jin-Liang Wang, Huai-Ning Wu, Tingwen Huang, Shun-Yan Ren, Pinning Control Strategies for Synchronization of Linearly Coupled Neural Networks With Reaction–Diffusion Terms IEEE Transactions on Neural Networks. ,vol. 27, pp. 749- 761 ,(2016) , 10.1109/TNNLS.2015.2423853
James M Tour, Tao He, None, Electronics: the fourth element. Nature. ,vol. 453, pp. 42- 43 ,(2008) , 10.1038/453042A